- Auvthu, Nagavardhini
- Cornish, Adam
- Li, You
- Mohammed, Akram
- Negi, Simarjeet
- Pandey, Sanjit
- Tanwir, Ahmad
- Vural, Suleyman
Description: I am currently working in a CRRI project, that involves the systems-level understanding of root metabolism in maize by comparative analysis of plant omics data and genetic variation, construction of predictive metabolic network models based on gene expression and metabolomics data and database design for maize root systems biology datasets
Title: Application of a hierarchical enzyme classification method reveals the role of gut microbiome in human metabolism
Description: The goal is to develop an enzyme classification method using functional and structural domains of proteins to distinguish the enzymes from non-enzyme sequences, followed by prediction of enzyme classes. The method will then be applied to predict all the enzymes from human gut metagenomic samples to investigate their roles in human metabolism using KEGG pathways.
Title: Functional characterization of healthy adult human brain and its application to study neurodevelopmental disorders
Description: The human brain is one of the most complex organs organized at multiple levels; numerous regional, structural and cellular segregations. Therefore, functionally characterizing the human brain is of utmost importance. In my project we are building an expandable-collapsible model of the adult human brain based on gene expression profiles at a very fine anatomical resolution. Then using this model as a reference, we study the transcriptional changes in neurodevelopment disorders and their impact on biological pathways.
Title: LocSigDb: A database of protein localization signals
Description: LocSigDB (http://genome.unmc.edu/LocSigDB/) is an exclusive, manually curated database of experimental protein localization signals for eight distinct subcellular locations in the eukaryotic cell. By performing extensive literature studies, we compiled a collection of 539 experimentally determined localization signals, along with the proteins they are found in. Each signal in the LocSigDB is annotated with its localization, source, PubMed references, and is linked to the proteins in the SwissProt database that contain the signal.
Title: A k-mer based method for identifying fusion genes from RNA-seq expression data
Description: Developing algorithms for detecting fusion gene events from RNA-seq data using an alignment-free method. The goal is to develop an open-source package that can efficiently and accurately detect fusion events from RNA-seq data.
Title: Exome analysis reveals differentially mutated gene signatures of stage, grade and subtype in breast cancers
Description: Discovering subtype-specific (ER+/-, PR+/-, HER2+/-) and Grade, Stage specific mutated genes/SNVs that might be responsible for breast cancer evolution and progression.
Title: Investigation of correlation between mutation load and phenotypic features of breast cancer patients
Description: Breast cancer is a leading cause of death amongst women and it is a heterogeneous disease that can be classified into several molecular subtypes. In this project, we use exome sequencing data to discover the mutation load of genes in patients of specific subtypes, and investigate the correlation to specific phenotypic features.
Title: Variant calling pipeline analysis using NIST Genome in a Bottle data
Description: There is a lot of variability in the efficacy of different variant calling pipelines and not a lot is known about which pipelines are the best. Given this, I hope to evaluate each pipeline by using the NIST Genome in a Bottle data as a golden standard against which I can compare each sets of a tools.
Title: N-gram based method to identify and quantify microbial communities in metagenomic samples.
Description: The goal of the project is to develop an efficient method to identify and quantify the organisms present in metagenomic samples at a strain level. The method will be developed into a standalone and online version for the research community to use.
Description: The primary goal of the project is to develop a database that will allow authorized lab members from multiple lab to insert and extract data without the help of IT professional or without having to go through the complex manual mapping process. This database will integrate different types of experimental data collected from the mouse models into one centralized database. The front– end user friendly web interface will allow to insert new data into the database, querying the database and generate reports. The back- end database will store all data in a relational format to enable specific querying of a particular type of data and extract information from multiple tables at once.